# What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation

arXiv: https://arxiv.org/abs/2010.14678 · HTML: https://mlsanity.com/paper/2010.14678/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models](https://mlsanity.com/paper/2203.06904/) (2022)
- [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://mlsanity.com/paper/2104.08835/) (2021)
- [Exploring Universal Intrinsic Task Subspace via Prompt Tuning](https://mlsanity.com/paper/2110.07867/) (2021)
- [NameGuess: Column Name Expansion for Tabular Data](https://mlsanity.com/paper/2310.13196/) (2023)
- [Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts](https://mlsanity.com/paper/2205.12701/) (2022)
- [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://mlsanity.com/paper/2302.08143/) (2023)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [BERT-based Acronym Disambiguation with Multiple Training Strategies](https://mlsanity.com/paper/2103.00488/) (2021)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)
- [SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions](https://mlsanity.com/paper/2202.09695/) (2022)
- [GLADIS: A General and Large Acronym Disambiguation Benchmark](https://mlsanity.com/paper/2302.01860/) (2023)
- [Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning](https://mlsanity.com/paper/2312.17055/) (2023)
- [Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models](https://mlsanity.com/paper/2508.09403/) (2025)
- [Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation](https://mlsanity.com/paper/2107.00316/) (2021)
- [MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction](https://mlsanity.com/paper/2202.09694/) (2022)
- [SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation](https://mlsanity.com/paper/2111.14306/) (2021)
- [Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog](https://mlsanity.com/paper/2112.08327/) (2021)
- [PSG: Prompt-based Sequence Generation for Acronym Extraction](https://mlsanity.com/paper/2111.14301/) (2021)
- [Generating Topic Pages for Scientific Concepts Using Scientific Publications](https://mlsanity.com/paper/2304.11922/) (2023)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [SciDr at SDU-2020: IDEAS -- Identifying and Disambiguating Everyday Acronyms for Scientific Domain](https://mlsanity.com/paper/2102.08818/) (2021)
- [Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuning](https://mlsanity.com/paper/2210.13311/) (2022)
- [Better than BERT but Worse than Baseline](https://mlsanity.com/paper/2105.05915/) (2021)
- [Domain Adaptive Pretraining for Multilingual Acronym Extraction](https://mlsanity.com/paper/2206.15221/) (2022)
- [ABB-BERT: A BERT model for disambiguating abbreviations and contractions](https://mlsanity.com/paper/2207.04008/) (2022)
- [Envisioning the Next-Gen Document Reader](https://mlsanity.com/paper/2302.07492/) (2023)

## Abstract (arXiv)

Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying acronyms and corresponding phrases (i.e., acronym identification (AI)) and finding the correct meaning of each acronym (i.e., acronym disambiguation (AD)) are crucial for text understanding. Despite the recent progress on this task, there are some limitations in the existing datasets which hinder further improvement. More specifically, limited size of manually annotated AI datasets or noises in the automatically created acronym identification datasets obstruct designing advanced high-performing acronym identification models. Moreover, the existing datasets are mostly limited to the medical domain and ignore other domains. In order to address these two limitations, we first create a manually annotated large AI dataset for scientific domain. This dataset contains 17,506 sentences which is substantially larger than previous scientific AI datasets. Next, we prepare an AD dataset for scientific domain with 62,441 samples which is significantly larger than the previous scientific AD dataset. Our experiments show that the existing state-of-the-art models fall far behind human-level performance on both datasets proposed by this work. In addition, we propose a new deep learning model that utilizes the syntactical structure of the sentence to expand an ambiguous acronym in a sentence. The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.